Protective Factor-Aware Dynamic Influence Learning for Suicide Risk Prediction on Social Media

TL;DR

This paper introduces a novel framework that predicts future suicide risk on social media by modeling the dynamic influence of both risk and protective factors over time, improving interpretability and prediction accuracy.

Contribution

It presents a new dataset and a dynamic influence learning model that jointly considers risk and protective factors, addressing limitations of prior work focused only on risk factors.

Findings

Model outperforms state-of-the-art methods across datasets.

Provides interpretable weights for risk and protective factors.

Captures temporal fluctuations in suicide risk.

Abstract

Suicide is a critical global health issue that requires urgent attention. Even though prior work has revealed valuable insights into detecting current suicide risk on social media, little attention has been paid to developing models that can predict subsequent suicide risk over time, limiting their ability to capture rapid fluctuations in individuals' mental state transitions. In addition, existing work ignores protective factors that play a crucial role in suicide risk prediction, focusing predominantly on risk factors alone. Protective factors such as social support and coping strategies can mitigate suicide risk by moderating the impact of risk factors. Therefore, this study proposes a novel framework for predicting subsequent suicide risk by jointly learning the dynamic influence of both risk factors and protective factors on users' suicide risk transitions. We propose a novel Protective Factor-Aware Dataset, which is built from 12 years of Reddit posts along with comprehensive annotations of suicide risk and both risk and protective factors. We also introduce a Dynamic Factors Influence Learning approach that captures the varying impact of risk and protective factors on suicide risk transitions, recognizing that suicide risk fluctuates over time according to established psychological theories. Our thorough experiments demonstrate that the proposed model significantly outperforms state-of-the-art models and large language models across three datasets. In addition, the proposed Dynamic Factors Influence Learning provides interpretable weights, helping clinicians better understand suicidal patterns and enabling more targeted intervention strategies.